The importance of implementation: Putting evaluation policy to work
Bibliographic record
Abstract
Abstract Federal agencies are increasingly expected to write and implement guidance for program evaluation, also known as evaluation policies. The Foundations for Evidence‐Based Policymaking Act required such policies for some federal agencies, and guidance from the White House Office of Management and Budget outlined an expectation that all agencies develop evaluation policies. Before these expectations, many federal agencies were already developing such policies to suit organizational needs and contexts. This chapter details findings from interviews with stakeholders at ten federal agencies and offices that developed and implemented evaluation policies before enacting the Foundations for Evidence‐Based Policymaking Act. These organizations represent early adopters of evaluation policies that can support future guidance and implementation of evaluation frameworks and capacity building in government. The study provides insight into the breadth and depth of the various strategies they used as well as their experiences with implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.614 | 0.632 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.024 | 0.069 |
| Scholarly communication | 0.068 | 0.079 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.028 | 0.041 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".